Comparison of predicted and measured resting metabolic rate among African-American men and women
Bibliographic record
Abstract
Indirect calorimetry has been established as the gold standard to measure resting metabolic rate (RMR); however, its clinical use is limited and can be very expensive. Therefore, the use of predictive equations are commonly used as an alternative. The objective of the current study was to compare RMR calculated using predictive models versus measured RMR using indirect calorimetry among African-Americans. African-American men and women, aged 21–70 years participated in the study. Participants were required to attend two study visits for the collection of self-reported and objective measurements of physical activity. Objective measures of physical activity were measured by accelerometer and self-reported physical activity was obtained using the International Physical Activity Questionnaire Long Form (IPAQ-LF). Objective measures of weight were measured using an automatic scale and height by stadiometer. Harris-Benedict, Nelson, Cunningham, Mifflin-St. Jeor, Owen and WHO/FAO/UNU models were used to measure RMR. All statistical analyses were conducted using R (version 4.3.3). The agreement between measured RMR and predicted RMR from the commonly used equations was assessed using the Bland-Altman method. The study comprised 64 African-American women ( n = 43, 67.2%) and men ( n = 19, 29.7%), with a mean age of 55.6 years. The WHO/FAO/UNU weight-and-height (bias = 20.5 kcal/day; 95% CI: −92.8 to 133.7; p = 0.719) and WHO/FAO/UNU weight-only equations (bias = 22.7 kcal/day; 95% CI: −90.2 to 135.7; p = 0.688) demonstrated the smallest, non-significant. The WHO/FAO/UNU model was more reliable than other models for predicting RMR among African-Americans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".